SNN-Based Lightweight Denoising Method for Event Cameras

Compared to image frames, the event stream captured by event cameras possesses the advantages of being high temporal resolution, sparsity and asynchronism. However, these characteristics also make it highly susceptible to noise which negatively affects the performance of downstream tasks. Existing event denoising methods are either too straightforward for varying noise-ratio scenes, or too complex, resulting in low efficiency and difficult to use in practice. In search of a denoising method that combines both performance and efficiency, we propose a lightweight and real-time event denoising algorithm based on the Spiking Neural Networks (SNN). Specifically, we introduce the Threshold-Limited PLIF neuron model, which leverages membrane potentials to capture the spatio-temporal correlations essential for effective denoising. With this neuron as the fundamental component, we design a frame-by-frame denoising network, called DeSNN. The proposed architecture utilizes the SNN architecture to integrate the spatio-temporal information from input event frames, and subsequently generates denoised outputs. Our method allows the entire processing pipeline to be maintained in the spiking data format, thereby fully exploiting the strengths of both dynamic vision sensors (DVS) and SNNs. Extensive experimental results demonstrated that our method achieves both high performance and high efficiency, which is beneficial for real-time downstream tasks. Furthermore, we have implemented our method in several simple real-world scenarios, illustrating its practical applicability and potential for deployment.